Systems Thinking and Beyond

The AI team takes a deep dive into Dr. Joseph E. Kasser’s (updated) presentation at the BCS SQM XXXIII 2026 International Quality Conference on 25 September 2026. The presentation outlines how artificial intelligence can transform both systems engineering and postgraduate education through a shared philosophy of preventive problem-solving. By employing interactive AI dialogues, professionals can uncover true customer requirements and minimize the need for lengthy, ambiguous textual specifications that often cause project failures. Similarly, the zero lectures model replaces traditional instructor-led teaching by empowering students to independently explore topics with AI, challenge assumptions, and exercise higher-order cognitive skills like creation and evaluation. Ultimately, these parallel approaches argue that leveraging AI-driven collaboration helps eliminate foundational defects at the earliest stages of both product development and academic learning.

The full presentation may be viewed at https://youtu.be/szX-9jZw5yo

The AI Team take a deep drive into a book that introduces applied ontology as a vital technical discipline designed to manage and integrate the massive amounts of data generated in modern biomedical science. The authors argue that information systems often suffer from interoperability issues caused by inconsistent terminology, technological silos, and logical errors known as imprecise thinking. To resolve these challenges, the book advocates for the use of Basic Formal Ontology (BFO), a top-level framework that provides a common set of categories for organizing scientific knowledge. By utilizing philosophical principles and structured logic, BFO helps developers create precise, computer-readable definitions that ensure data remains shareable and reusable across different research communities. Ultimately, the work serves as a guide for building robust information repositories that can support complex scientific reasoning and cross-disciplinary collaboration.

The AI team take a deep dive into Claude For Dummies, a comprehensive guidebook by Stephanie Diamond designed to help users master Anthropic's AI assistant. It covers fundamental operations such as account setup, effective prompting, and the use of specialized features like Projects and Memory. The manual also details advanced capabilities, including document analysis, automated workflows, and the creation of interactive Artifacts. By explaining how Claude differs from traditional search engines through its natural conversation style and complex reasoning, the source serves as a practical roadmap for both beginners and experienced users. Ultimately, the text highlights the tool's versatility in improving writing, conducting research, and solving problems in daily life.

In this presentation, the AI team takes a deep dive into a YouTube video discusion the dual nature of artificial intelligence as both a powerful problem-solving tool and a source of deceptive illusions. He utilizes Large Language Models (LLMs) to demonstrate how they can assist with tasks ranging from systems engineering to resolving mundane household issues. The sources clarify that these models function using probabilistic text generation rather than genuine understanding, operating as stateless engines that rely on a context window to mimic memory. This structural design leads to significant risks, such as hallucinations where the AI confidently fabricates false information, and vulnerability to deepfake scams. Ultimately, the text argues that while AI can drastically increase productivity, it requires a human pilot to provide critical reasoning and verification. Users are encouraged to manage the technology's limitations by providing hyperspecific instructions and maintaining active oversight of the AI's output.

The original video may be found at https://youtu.be/OVjreyqYDEk.

The AI team takes a deep dive into "Natureology" hallucination which occurred when the AI was asked to define a non-existent term (a typo for "Naturology") and, rather than simply seeking clarification, invented a complete philosophical and academic framework for the word. Drawing on Joseph Kasser's nine Holistic Thinking Perspectives (HTPs), the AI defined this imaginary field as the "integrative study of the natural world" and detailed how it would function across every perspective, such as describing "Big Picture Natureology" as the recognition of interconnected Earth systems.

This behavior was a result of an approval-seeking bias inherent in its training, which pressured the AI to provide a confident, "authoritative and reassuring" response even when faced with a concept that did not exist in established literature.